Issue:A modifying operator for temporal intuitionistic fuzzy index matrices with an application to autonomous self-managing databases

shortcut

http://ifigenia.org/wiki/issue:nifs/32/1/30-39

Title of paper: A modifying operator for temporal intuitionistic fuzzy index matrices with an application to autonomous self-managing databases Author(s): Veselina Bureva     0000-0003-4344-4392 Laboratory of Intelligent Systems, Burgas State University “Prof. Dr. Assen Zlatarov”, 1 “Prof. Yakimov” Blvd., Burgas 8010, Bulgaria veselina-bureva@uniburgas.bg Krassimir Atanassov     0000-0001-5625-071X Department of Bioinformatics and Mathematical Modelling, Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Acad. G. Bonchev Str., Bl. 105, Sofia-1113, Bulgaria krat@bas.bg , k.t.atanassov@gmail.com Published in: Notes on Intuitionistic Fuzzy Sets, Volume 32 (2026), Number 1, pages 30–39 DOI: https://doi.org/10.7546/nifs.32.1.30-39 Download:  PDF (1319  Kb, File info) Abstract: In the current investigation, a modifying operator over temporal intuitionistic fuzzy index matrices (TIFIM) is presented. Thereafter, an example of autonomous priority-based self-managing database system using intuitionistic fuzzy logic is developed. The modifying operator is implemented for priorities updating. The priorities are changed according to three cases. In the first case, the modifying operator increases the degree of membership and decreases the degree of non-membership if the record is frequently searched. In the second case, the modifying operator decreases the degree of membership and increases the degree of non-membership if the record is rarely searched. In the third case, the modifying operator set the record priority to ⟨0,1⟩ and deleted it the record is not searched more than n days. Keywords: Autonomous self-managing database, Dynamic priorities, Temporal intuitionistic fuzzy index matrix. AMS Classification: 03E72 References: Abbasi, M., Bernardo, M. V., Váz, P., Silva, J., & Martins, P. (2024). Adaptive and scalable database management with machine learning integration: A PostgreSQL case study. Information, 15(9), Article 574. Atanassov, K. (1998). Generalized Nets in Artificial Intelligence. Volume 1: Generalized Nets and Expert Systems. “Prof. Marin Drinov” Publishing House of the Bulgarian Academy of Sciences, Sofia. Atanassov, K. (1999). Intuitionistic Fuzzy Sets: Theory and Applications. Springer, Heidelberg. Atanassov, K. (2012). On Intuitionistic Fuzzy Sets Theory. Springer, Berlin. Atanassov, K. (2014). Index Matrices: Towards an Augmented Matrix Calculus. Studies in Computational Intelligence Series, Vol. 573, Springer, Cham. Atanassov, K. (2020). A generalized net model of an intuitionistic fuzzy expert system. Notes on Intuitionistic Fuzzy Sets, 26(1), 46–68. Atanassov, K., Szmidt, E., & Kacprzyk, J. (2013). On intuitionistic fuzzy pairs. Notes on Intuitionistic Fuzzy Sets, 19(3), 1–13. Avula, S. B. (2025). Oracle autonomous database for transforming database administration by harnessing artificial intelligence. In: Global Conference in Emerging Technology, Pune, India, 2025, 1–7, doi: 10.1109/GINOTECH63460.2025.11076743. Bureva, V., Petrov, P., Andonov, V., & Atanassov, K. (2023). Intuitionistic fuzzy evaluation of user requests frequency. In: Atanassov, K. T., et al. (Eds.) Uncertainty and Imprecision in Decision Making and Decision Support - New Advances, Challenges, and Perspectives. IWIFSGN BOS/SOR 2022. Lecture Notes in Networks and Systems, Vol. 793, pp. 15 – 21. Springer, Cham. Chountas, P., Atanassov, K., Sotirova, E., & Bureva, V. (2016). Generalized net model of an expert system dealing with temporal hypothesis. In: Andreasen, T., Christiansen, H., Kacprzyk, J., Larsen, H., Pasi, G., Pivert, O., De Tré, G., Vila, M. A., Yazici, A., Zadrożny, S. (Eds.) Flexible Query Answering Systems 2015. Advances in Intelligent Systems and Computing, Vol. 400, 473–481. Springer, Cham. Helskyaho, H., Yu, J., & Yu, K. (2021). Oracle autonomous database for machine learning. In: Machine Learning for Oracle Database Professionals, pp. 97–133. Apress, Berkeley, CA. Kavuluri, H., Avula, S. B., & Sirimalla, A. (2025). Performance tuning for cloud-based databases Oracle/Postgres: Analyzing query optimization techniques. Journal of Wireless Mobile Networks, 16(2), 459–475. Kolev, B., El-Darzi, E., Sotirova, E., Petrounias, I., Atanassov, K., Chountas, P., & Kodogiannis, V. (2006). Generalized Nets in Artificial Intelligence. Volume 3: Generalized Nets, Relation Databases and Expert Systems. “Prof. Marin Drinov” Publishing House of the Bulgarian Academy of Sciences, Sofia. Kossmann, J., & Schlosser, R. (2019). A framework for self-managing database systems. In: 2019 IEEE 35th International Conference on Data Engineering Workshops (ICDEW), Macao, China, pp. 100–106. Oloruntoba, O. (2025). AI-Driven autonomous database management: Self-tuning, predictive query optimization, and intelligent indexing in enterprise IT environments. World Journal of Advanced Research and Reviews, 25(02), 1558–1580. Sharma, B M., Krishnakumar, K .M. & Panda, R. (2022). Oracle Autonomous Database in Enterprise Architecture: Utilize Oracle Cloud Infrastructure Autonomous Databases for better consolidation, automation, and security. Packt Publishing, Birmingham. Sheikhkhoshkar, M., El-Haouzi, H., Aubry, A., Hamzeh, F., & Rahimian, F. (2025). A data-driven and knowledge-based decision support system for optimized construction planning and control. Automation in Construction, 173, Article ID 106066. Citations: The list of publications, citing this article may be empty or incomplete. If you can provide relevant data, please, write on the talk page.